Advanced Driver Assistance Systems (ADAS) include systems such as, Forward Collision Warning (FCW), Blind Spot Warning (BLIS), Adaptive Cruise Control (ACC), and Lane Departure Warning (LDW). The systems monitor the vehicle environment and traffic conditions, typically taking measurements of objects using radar or camera-based sensors, to assist the driver. The systems typically classify object measurements into categories of valid targets (i.e., vehicles, pedestrians, lane markings, etc.) or noise (i.e., any measurement from items of non-interest such as environmental objects or road infrastructure). Further, the systems look for certain measurement signatures that tend to indicate the object as “valid”. When an object, or target, is classified as “valid”, the systems will determine whether the valid object is a threat that is sufficient enough to require driver assistance.
The process of detection, classification, and threat assessment has several issues and limitations. For example, the noise level, as perceived by radars or cameras, is highly dependent upon the environmental objects and road infrastructure. For a classification strategy that is non-location and non-history based, there is the potential for false classification. An object may be falsely classified as noise when it is actually a valid target. Likewise, an object may be falsely classified as a valid target, when it is, in fact, noise. Similarly, for a threat assessment strategy that is non-location and non-history based, there is also the potential for inaccurate threat assessment.
There is a need to improve the feature performance of an Advanced Driver Assistance System by increasing the reliability of the classification strategy and/or threat assessment strategy.